面孔生成¶
在这个项目中，您将使用生成的对抗网络来生成新的面孔图像。
获取数据¶
您将在此项目中使用两个数据集：
- MNIST
- CelebA
由于celebA数据集很复杂，而且您首次在项目中执行GAN，我们希望您在CelebA之前测试MNIST上的神经网络。 在MNIST上运行GAN可以让您了解您的模型如何更快地训练。
如果您使用[FloydHub]（https://www.floydhub.com/ ），将“data_dir”设置为“/ input”，并使用[FloydHub数据ID]（http://docs.floydhub.com/home/using_datasets/ ）“R5KrjnANiKVhLWAkpXhNBe”。
In [1]:
data_dir = './data'
# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
探索数据¶
MNIST¶
如您所知，[MNIST]（http://yann.lecun.com/exdb/mnist/ ）数据集包含手写数字的图像。 您可以通过更改“show_n_images”来查看第一个示例数。
In [2]:
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
CelebA¶
[CelebFaces属性数据集（CelebA）]（http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html ）数据集包含超过20万名有注释的名人图片。 由于您要生成面孔，因此您不需要注释。 您可以通过更改“show_n_images”来查看第一个示例数。
In [3]:
show_n_images = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
预处理数据¶
由于该项目的主要重点是构建GAN，因此我们将为您处理数据。 MNIST和CelebA数据集的值将在28×28维度图像的-0.5至0.5的范围内。 CelebA图像将被裁剪以去除不包括脸部的图像部分，然后调整到28x28。
MNIST图像是具有单个[颜色通道]（https://en.wikipedia.org/wiki/Channel_(digital_image%29) ）的黑白图像，而CelebA图像具有[3色通道（RGB颜色通道）]（https://en.wikipedia.org/wiki/Channel_(digital_image%29#RGB_Images)。 建立神经网络 您将通过实现以下功能构建构建GAN所需的组件：
model_inputsdiscriminatorgeneratormodel_lossmodel_opttrain
检查TensorFlow版本和访问GPU¶
这将检查以确保您具有正确版本的TensorFlow和访问GPU
In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer. You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
输入¶
实现model_inputs函数来创建神经网络的TF占位符。 应该创建以下占位符：
- Real input images placeholder with rank 4 using
image_width,image_height, andimage_channels. - Z input placeholder with rank 2 using
z_dim. - Learning rate placeholder with rank 0.
在以下元组中返回占位符（实际输入图像的张量，z数据的张量）
In [5]:
import problem_unittests as tests
def model_inputs(image_width, image_height, image_channels, z_dim):
"""
Create the model inputs
:param image_width: The input image width
:param image_height: The input image height
:param image_channels: The number of image channels
:param z_dim: The dimension of Z
:return: Tuple of (tensor of real input images, tensor of z data, learning rate)
"""
real_input = tf.placeholder(tf.float32,shape=(None,image_width,image_height,image_channels))
z = tf.placeholder(tf.float32,shape=(None,z_dim))
learning_rate = tf.placeholder(tf.float32)
return real_input, z, learning_rate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
鉴别器¶
实现“鉴别器”来创建一个区分“图像”的鉴别器神经网络。 该函数应该能够重用神经网络中的变量。 使用范围名称为“discriminator”的tf.variable_scope来允许重用这些变量。 该函数应该返回一个元组（鉴别器的张量输出，鉴别器的张量逻辑）。
In [6]:
def discriminator(images, reuse=False):
"""
Create the discriminator network
:param images: Tensor of input image(s)
:param reuse: Boolean if the weights should be reused
:return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
"""
alpha = 0.1
keep_prob = 0.5
with tf.variable_scope('discriminator',reuse=reuse):
conv1 = tf.layers.conv2d(images,64,5,strides=2,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
conv1 = tf.maximum(conv1*alpha,conv1)
conv2 = tf.layers.conv2d(conv1,128,5,strides=2,padding='same', kernel_initializer= tf.contrib.layers.xavier_initializer())
conv2 = tf.layers.batch_normalization(conv2,training=True)
conv2 = tf.maximum(conv2*alpha,conv2)
conv2 = tf.nn.dropout(conv2,keep_prob=keep_prob)
conv3 = tf.layers.conv2d(conv2,256,5,strides=2,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
conv3 = tf.layers.batch_normalization(conv3,trainable=True)
conv3 = tf.maximum(conv3*alpha,conv3)
conv3 = tf.nn.dropout(conv3,keep_prob=keep_prob)
flat = tf.reshape(conv3, (-1, 4*4*256))
logits = tf.layers.dense(flat, 1)
out = tf.sigmoid(logits)
return out, logits
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
生成器¶
实现generator来生成一个使用z的图像。 该函数应该能够重用神经网络中的变量。 使用范围名称为“generator”的tf.variable_scope来允许重用这些变量。 该函数应返回生成的28 x 28 xout_channel_dim图像。
In [8]:
def generator(z, out_channel_dim, is_train=True):
"""
Create the generator network
:param z: Input z
:param out_channel_dim: The number of channels in the output image
:param is_train: Boolean if generator is being used for training
:return: The tensor output of the generator
"""
alpha_temp =0.1
keep_prob = 0.5
with tf.variable_scope('generator',reuse= not is_train):
x = tf.layers.dense(z, 7*7*1024)
x = tf.reshape(x,(-1, 7, 7, 1024))
x = tf.layers.batch_normalization(x,training=is_train)
x = tf.maximum(x*alpha_temp,x)
conv1 = tf.layers.conv2d_transpose(x,512,5,strides=2,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
conv1 = tf.layers.batch_normalization(conv1,training=is_train)
conv1 = tf.maximum(conv1*alpha_temp,conv1)
conv1 = tf.nn.dropout(conv1,keep_prob=keep_prob)
conv2 = tf.layers.conv2d_transpose(conv1,256,5,strides=2,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
conv2 = tf.layers.batch_normalization(conv2,training=is_train)
conv2 = tf.maximum(conv2*alpha_temp,conv2)
conv2 = tf.nn.dropout(conv2,keep_prob=keep_prob)
logits = tf.layers.conv2d_transpose(conv2,out_channel_dim,5,strides=1,padding='same',kernel_initializer= tf.contrib.layers.xavier_initializer())
out = tf.tanh(logits)
return out
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Loss¶
实施“model_loss”来构建GAN进行训练并计算损失。 该函数应该返回一个元组（鉴别器丢失，发生器丢失）。 使用您实现的以下功能：
discriminator（images，reuse = False）generator（z，out_channel_dim，is_train = True）
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
"""
Get the loss for the discriminator and generator
:param input_real: Images from the real dataset
:param input_z: Z input
:param out_channel_dim: The number of channels in the output image
:return: A tuple of (discriminator loss, generator loss)
"""
smooth = 0.1
g_model = generator(input_z, out_channel_dim)
d_model_real, d_logits_real = discriminator(images = input_real)
d_model_fake, d_logits_fake = discriminator(images = g_model, reuse = True)
d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_real,
labels = tf.ones_like(d_model_real)*(1-smooth)))
d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
labels = tf.zeros_like(d_model_fake)))
g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits = d_logits_fake,
labels = tf.ones_like(d_model_fake)))
d_loss = d_loss_real + d_loss_fake
return (d_loss, g_loss)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
优化¶
实现model_opt来创建GAN的优化操作。 使用tf.trainable_variables获取所有可培训的变量。 用名称和标识符和生成器范围名称过滤变量。 该功能应该返回一个元组（鉴别器训练操作，发电机训练操作）。
In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
"""
Get optimization operations
:param d_loss: Discriminator loss Tensor
:param g_loss: Generator loss Tensor
:param learning_rate: Learning Rate Placeholder
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:return: A tuple of (discriminator training operation, generator training operation)
"""
t_vars = tf.trainable_variables()
d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
g_vars = [var for var in t_vars if var.name.startswith('generator')]
# Optimize
with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 = beta1).minimize(d_loss, var_list = d_vars)
g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1 =beta1).minimize(g_loss, var_list = g_vars)
return d_train_opt, g_train_opt
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np
def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
"""
Show example output for the generator
:param sess: TensorFlow session
:param n_images: Number of Images to display
:param input_z: Input Z Tensor
:param out_channel_dim: The number of channels in the output image
:param image_mode: The mode to use for images ("RGB" or "L")
"""
cmap = None if image_mode == 'RGB' else 'gray'
z_dim = input_z.get_shape().as_list()[-1]
example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])
samples = sess.run(
generator(input_z, out_channel_dim, False),
feed_dict={input_z: example_z})
images_grid = helper.images_square_grid(samples, image_mode)
pyplot.imshow(images_grid, cmap=cmap)
pyplot.show()
Train¶
实施“train”来建设和训练GAN。 使用您实现的以下功能：
model_inputs（image_width，image_height，image_channels，z_dim）model_loss（input_real，input_z，out_channel_dim）model_opt（d_loss，g_loss，learning_rate，beta1）
在训练时使用show_generator_output显示generator输出。 为每个批次运行“show_generator_output”将大大增加培训时间并增加笔记本的大小。 建议每100批打印一次“发生器”输出。
In [25]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
"""
Train the GAN
:param epoch_count: Number of epochs
:param batch_size: Batch Size
:param z_dim: Z dimension
:param learning_rate: Learning Rate
:param beta1: The exponential decay rate for the 1st moment in the optimizer
:param get_batches: Function to get batches
:param data_shape: Shape of the data
:param data_image_mode: The image mode to use for images ("RGB" or "L")
"""
steps = 0
if (data_image_mode == "L"):
out_channel_dim = 1
else:
out_channel_dim = 3
print_every = 10
show_every = 100
samples, losses = [],[]
inputs_real, inputs_z, lr = model_inputs(data_shape[1],
data_shape[2],
data_shape[3],
z_dim)
lr = learning_rate
d_loss, g_loss = model_loss(inputs_real,
inputs_z,
out_channel_dim)
d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(epoch_count):
for batch_images in get_batches(batch_size):
steps += 1
# TODO: Train Model
batch_images = batch_images *2
batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
_ = sess.run(d_opt, feed_dict={inputs_real: batch_images,
inputs_z: batch_z})
_ = sess.run(g_opt, feed_dict={inputs_z: batch_z,
inputs_real: batch_images})
if steps % print_every == 0:
train_loss_d = d_loss.eval({inputs_z: batch_z, inputs_real: batch_images})
train_loss_g = g_loss.eval({inputs_z: batch_z})
print("Epoch {}/{}...".format((epoch_i+1), epoch_count),
"Discriminator Loss: {:.4f}".format(train_loss_d),
"Generator Loss: {:.4f}".format(train_loss_g))
#losses.append((train_loss_d,train_loss_g))
if steps % show_every == 0:
show_generator_output(sess, 9, inputs_z, data_shape[3],
data_image_mode)
MNIST¶
在MNIST上测试您的GAN架构。 经过2个纪元，GAN应该能够生成看起来像手写数字的图像。 确保发电机的损耗低于鉴别器的损耗或接近0。
In [26]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2
mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
mnist_dataset.shape, mnist_dataset.image_mode)
CelebA¶
在CelebA上运行你的GAN。 平均GPU需要大约20分钟才能运行一个时代。 您可以运行整个时代，或者当它开始生成逼真的面孔时停止。
In [27]:
batch_size = 64
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1
celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
celeba_dataset.shape, celeba_dataset.image_mode)
提交此项目¶
提交此项目时，请确保在保存笔记本计算机之前运行所有单元格。 将笔记本文件保存为“dlnd_face_generation.ipynb”，并将其另存为“File” - >“Download as”下的HTML文件。 在您的提交中包含“helper.py”和“problem_unittests.py”文件。
In [ ]: